The stochastic modified Lundqvist-Korf diffusion process: statistical and computational aspects and application to modeling of the $$\hbox {CO}_2$$ emission in Morocco

Nafidi, Ahmed; Azri, Abdenbi El; Sánchez, Ramón Gutiérrez

doi:10.1007/s00477-021-02089-8

Cited by 9 publications

(4 citation statements)

References 30 publications

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“… 2016 ; Nafidi et al. 2022 ; Pell et al. 2018 ) (Zika, Ebola, COVID-19 and CO emissions, with certain exponential growths) and turn out to be successful in our context of offenses.…”

Section: Discussionmentioning

confidence: 96%

Spatio-temporal stochastic differential equations for crime incidence modeling

Calatayud

Jornet

Mateu

2023

Stoch Environ Res Risk Assess

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We propose a methodology for the quantitative fitting and forecasting of real spatio-temporal crime data, based on stochastic differential equations. The analysis is focused on the city of Valencia, Spain, for which 90247 robberies and thefts with their latitude-longitude positions are available for a span of eleven years (2010–2020) from records of the 112-emergency phone. The incidents are placed in the 26 zip codes of the city (46001–46026), and monthly time series of crime are built for each of the zip codes. Their annual-trend components are modeled by Itô diffusion, with jointly correlated noises to account for district-level relations. In practice, this study may help simulate spatio-temporal situations and identify risky areas and periods from present and past data.

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“… 2016 ; Nafidi et al. 2022 ; Pell et al. 2018 ) (Zika, Ebola, COVID-19 and CO emissions, with certain exponential growths) and turn out to be successful in our context of offenses.…”

Section: Discussionmentioning

confidence: 96%

Spatio-temporal stochastic differential equations for crime incidence modeling

Calatayud

Jornet

Mateu

2023

Stoch Environ Res Risk Assess

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“…( 2022 ) proposes multiple stochastic logistic functions to fit several COVID-19 waves and forecast; and Nafidi et al. ( 2022 ) studies the applicability of a stochastic modified Lundqvist–Korf diffusion process to model emissions. As our paper shows, differential equation-based statistical models shall be considered a tool to assess the evolution of social behaviors.…”

Section: Discussionmentioning

confidence: 99%

Modeling noisy time-series data of crime with stochastic differential equations

Calatayud

Jornet

Mateu

2022

Stoch Environ Res Risk Assess

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We develop and calibrate stochastic continuous models that capture crime dynamics in the city of Valencia, Spain. From the emergency phone, data corresponding to three crime events, aggressions, stealing and women alarms, are available from the year 2010 until 2020. As the resulting time series, with monthly counts, are highly noisy, we decompose them into trend and seasonality parts. The former is modeled by geometric Brownian motions, both uncorrelated and correlated, and the latter is accommodated by randomly perturbed sine-cosine waves. Albeit simple, the models exhibit high ability to simulate the real data and show promising for crimes-interaction identification and short-term predictive policing.

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“…On the other hand, diffusion stochastic processes (SDPs) are good mathematical models that describe probabilistic phenomena in several domains, like environment, biology, economics, medicines and others. Some of the SDPs studied in this sense are found in the works of Bertalanffy [1], Gamma [2], Weibull [3] and Lundqvist-Korf [4], who used SDPs to study growth patterns because of their exponential behavior. In addition, Capocelli and Ricciardi [5] were the first to consider a diffusion process that was associated with the Gompertz curve, and this has been applied to several fields of study, such as population growth or neural activity modeling [6].…”

Section: Introductionmentioning

confidence: 99%

“…The basic case for this approach can be found in Bibby and Sorensen [16], as well as in Ait-Sahalia [17]. The method of estimating the ML of the parameters when using likelihood equations can be difficult to implement, which is the reason why we propose to use the simulated annealing (SA) method for estimating the parameters in an SDP (see, for instance, [3,4]).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process

Nafidi,

Chakroune,

Gutiérrez-Sánchez

et al. 2023

Fractal Fract

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In this work, we study the possibility of using a new non-homogeneous stochastic diffusion process based on the Rayleigh density function to model the evolution of the active cases of COVID-19 in Morocco. First, the main probabilistic characteristics and analytic expression of the proposed process are obtained. Next, the parameters of the model are estimated by the maximum likelihood methodology. This estimation and the subsequent statistical inference are based on the discrete observation of the variable x(t) “number of active cases of COVID-19 in Morocco” by using the data for the period of 28 January to 4 March 2022. Then, we analyze the mean functions by using simulated data for fit and forecast purposes. Finally, we explore the illustration of using this new process to fit and forecast the active cases of COVID-19 data.

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The stochastic modified Lundqvist-Korf diffusion process: statistical and computational aspects and application to modeling of the $$\hbox {CO}_2$$ emission in Morocco

Cited by 9 publications

References 30 publications

Spatio-temporal stochastic differential equations for crime incidence modeling

Spatio-temporal stochastic differential equations for crime incidence modeling

Modeling noisy time-series data of crime with stochastic differential equations

Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process

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